Long Beach Animal Shelter: Stay Duration and Outcomes

Animals with shorter shelter stays correlate with improved adoption outcomes

TidyTuesday
Data Visualization
R Programming
2025
Analysis of Long Beach Animal Shelter data reveals a striking correlation between length of stay and animal outcomes. This visualization explores how different animal types experience varying shelter durations and how outcome trends have evolved from 2017-2024, demonstrating a dramatic increase in adoption rates alongside decreased euthanasia. The data tells a success story of improving shelter practices while highlighting challenges for specific animal types.
Author

Steven Ponce

Published

February 27, 2025

Figure 1: A two-panel visualization of Long Beach Animal Shelter data. The left panel shows density distributions of the length of stay by animal type, with birds and reptiles having the shortest stays (median 0-3 days) and rabbits the longest (median 16 days). The right panel displays outcome trends from 2017-2024, showing adoption rates more than tripled to 32.8% while euthanasia rates decreased from 21.3% to 16.2%. Colors indicate positive outcomes (green), neutral (yellow), and negative (red).

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
   tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder,      # Record Your Plot History 
    patchwork,      # The Composer of Plots # The Composer of Plots # The Composer of Plots
    ggridges        # Ridgeline Plots in 'ggplot2'
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  14,
    height =  12,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
tt <- tidytuesdayR::tt_load(2025, week = 09) 

longbeach <- tt$longbeach |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

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glimpse(longbeach)
skim(longbeach)

4. Tidy Data

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### |-  tidy data ----
longbeach_clean <- longbeach |>  
    mutate(
        # Time-based variables
        outcome_year = year(outcome_date),
        outcome_month = month(outcome_date),
        outcome_season = case_when(
            outcome_month %in% c(12, 1, 2) ~ "Winter",
            outcome_month %in% c(3, 4, 5) ~ "Spring",
            outcome_month %in% c(6, 7, 8) ~ "Summer",
            outcome_month %in% c(9, 10, 11) ~ "Fall",
            TRUE ~ NA_character_  
        ),
        # Duration calculations
        days_in_shelter = as.numeric(difftime(outcome_date, intake_date, units = "days")),
        age_at_outcome = if_else(
            !is.na(dob), 
            as.numeric(difftime(outcome_date, dob, units = "days")) / 365.25,
            NA_real_
        )
    )

# P1. Days in Shelter by Animal Type ----

# Filter data for the visualization with clear criteria
shelter_stay <- longbeach_clean |>
    filter(
        !is.na(days_in_shelter), 
        days_in_shelter >= 0, 
        days_in_shelter <= 365,
        animal_type %in% c("dog", "cat", "rabbit", "bird", "reptile")
    ) |> 
    mutate(animal_type = str_to_title(animal_type))

# Calculate summary statistics with proper grouping
median_stays <- shelter_stay |>
    group_by(animal_type) |>
    summarize(
        median_stay = median(days_in_shelter, na.rm = TRUE),
        max_density = 0.02,  
        .groups = 'drop'  
    )

# Join and transform in a clear sequence
shelter_stay <- shelter_stay |>
    left_join(
        median_stays |> select(animal_type, median_stay), 
        by = "animal_type"
    ) |>
    mutate(
        animal_type = fct_reorder(animal_type, median_stay, .desc = TRUE)
    )

# Ensure consistent factor levels across dataframes
median_stays <- median_stays |>
    mutate(
        animal_type = factor(animal_type, levels = levels(shelter_stay$animal_type))
    )

# P2. Outcome Trends by Type ----
outcome_by_year <- longbeach_clean |>
    filter(!is.na(outcome_year), !is.na(outcome_type)) |>
    count(outcome_year, outcome_type) |>
    group_by(outcome_year) |>
    mutate(pct = n / sum(n) * 100) |>
    ungroup() |>
    # Keep only main outcome types of interest
    filter(outcome_type %in% c("adoption", "euthanasia", "return to owner", "transfer", "died")) |>
    mutate(
        # Categorize outcomes
        outcome_category = case_when(
            outcome_type %in% c("adoption", "return to owner") ~ "Positive",
            outcome_type %in% c("euthanasia", "died") ~ "Negative",
            TRUE ~ "Neutral"
        ),
        outcome_type = str_to_title(outcome_type),
        # Create category factor with explicit ordering
        outcome_category = factor(
            outcome_category, 
            levels = c("Positive", "Neutral", "Negative")
        ),
        # Create descriptive facet labels
        facet_label = paste0(outcome_type, " (", outcome_category, ")"),
        # Create ordered factor for outcome types
        outcome_type = factor(
            outcome_type, 
            levels = c("Adoption", "Return To Owner", "Transfer", "Died", "Euthanasia"),
            ordered = TRUE
        )
    ) |> 
    filter(!is.na(facet_label)) |> 
    # Create ordered faceting variable
    mutate(
        # Prefix with numeric category for sorting
        facet_order = paste0(
            as.numeric(outcome_category), "_", 
            outcome_type, " (", outcome_category, ")"
        ),
        # Convert to factor with proper ordering
        facet_order = factor(
            facet_order, 
            levels = unique(facet_order[order(outcome_category, outcome_type)])
        )
    )

# Extract first and last points for each outcome type for highlighting
endpoints <- outcome_by_year |>
    group_by(outcome_type) |>
    slice(c(1, n())) |>  
    ungroup()

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
# Color for P1
colors <- get_theme_colors(palette = c(
    "Rabbit" = "#FDCDAC",  
    "Cat" = "#F4A582",     
    "Dog" = "#D6604D",     
    "Reptile" = "#2C7FB8", 
    "Bird" = "#7FCDBB"      
))

# Colors for P2
colors2 <- get_theme_colors(palette = c(
    "Positive" = alpha("#1A8754", 0.9),  
    "Neutral" = alpha("#FFC107", 0.9),   
    "Negative" = alpha("#DC3545", 0.9)
))

### |-  titles and caption ----
title_text <- str_glue("Long Beach Animal Shelter: Stay Duration and Outcomes")

subtitle_text <- str_glue("Animals with shorter shelter stays correlate with improved adoption outcomes")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 09,
    source_text =  "City of Long Beach Animal Care Services" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Text styling 
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_text(color = colors$text, size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(color = "grey95", linewidth = 0.1),
        
        # Legend elements
        legend.position = "plot",
        legend.title = element_text(family = fonts$text, size = rel(0.8)),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # Plot margins 
        plot.margin = margin(t = 10, r = 10, b = 10, l = 10),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot  ----
# P1. Days in Shelter by Animal Type ----
p1 <- ggplot(shelter_stay, aes(x = days_in_shelter, y = animal_type, fill = animal_type)) +
    # Geoms
    geom_density_ridges(
        alpha = 0.8, 
        scale = 1.5,
        rel_min_height = 0.01,
        bandwidth = 2.5,
        quantile_lines = TRUE,
        quantiles = 2  # median
    ) +
    geom_point(
        data = median_stays,
        aes(x = median_stay, y = animal_type),
        shape = 21,
        fill = "white",
        color = "black",
        size = 4,        
        stroke = 1.5     
    ) +
    geom_label(        
        data = median_stays,
        aes(x = median_stay, y = animal_type, 
            label = paste0("Median: ", round(median_stay, 1), " days")),
        hjust = -0.1,
        vjust = 0.5,
        size = 3.5,      # Slightly larger
        fontface = "bold",
        fill = alpha("white", 0.7),
        label.padding = unit(0.2, "lines"),
        label.r = unit(0.15, "lines")
    ) +
    # Scales
    scale_x_continuous(
        limits = c(-10, 100),
        breaks = seq(0, 100, by = 20),
        minor_breaks = seq(0, 100, by = 10),
        expand = c(0, 0)
    ) +
    scale_y_discrete(expand = c(0, 0.25)) +
    scale_fill_manual(values = colors$palette) +
    coord_cartesian(clip = 'off') +
    # Labs
    labs(
        title = "Length of Stay in Shelter by Animal Type",
        subtitle = str_wrap("Distribution of days between intake and outcome (ordered by median stay duration)", width = 60),
        x = "Days in Shelter",
        y = NULL,
    ) +
    # Theme
    theme(
        panel.grid.major = element_line(color = "gray", linewidth = 0.1),
        axis.text.y = element_text(face = "bold")
    ) 

# P2. Outcome Trends by Type ----
p2 <- ggplot(outcome_by_year, aes(x = outcome_year, y = pct, color = outcome_category)) +
    # Geoms
    geom_line(size = 1.5) + 
    geom_point(size = 2.5) +  
    geom_point(data = endpoints, size = 5, shape = 21, fill = "white", stroke = 2) +
    geom_text(
        data = endpoints,
        aes(label = sprintf("%.1f%%", pct)),
        hjust = ifelse(endpoints$outcome_year == min(endpoints$outcome_year), -0.3, 1.3),
        vjust = -1.5,
        size = 3.5,  # Larger text
        fontface = "bold",
        show.legend = FALSE
    ) +
    geom_text(
        data = tibble(
            outcome_year = 2021.6,
            pct = 15,
            facet_order = unique(outcome_by_year$facet_order)[1] # Only first panel
        ),
        label = "Adoptions more than tripled\nwhile euthanasia decreased",
        size = 3,
        fontface = "italic",
        color = "gray30",
        hjust = 0
    ) +
    # Scales
    scale_x_continuous(breaks = c(2017, 2021, 2024), expand = expansion(mult = c(0.1, 0.1))) +
    scale_y_continuous(
        limits = c(0, 45),
        breaks = seq(0, 40, by = 20),
        labels = function(x) paste0(x, "%"),
        minor_breaks = NULL
    ) +
    scale_color_manual(values = colors2$palette) +
    coord_cartesian(clip = 'off') +
    # Labs
    labs(
        title = "Outcome Trends by Type (2017-2024)",
        subtitle = str_wrap("Positive outcomes have increased while negative outcomes have decreased", width = 60),
        x = "Year",
        y = "Percentage of Animals (%)",
    ) +
    # Facets
    facet_wrap(~ facet_order, scales = "fixed", ncol = 1, drop = TRUE, 
               labeller = labeller(facet_order = function(x) gsub("^\\d+_", "", x))) +
    # Theme
    theme(
        strip.text = element_text(face = "bold", family = fonts$text, size = rel(0.8)),
        strip.background = element_rect(fill = "gray95", color = NA),
        panel.grid.major.y = element_line(color = "gray", linewidth = 0.1),
        panel.spacing = unit(1.5, "lines")
    ) 

# Combined Plot -----
combined_plot <- (p1 | plot_spacer() | p2) +
    plot_layout(widths = c(1, 0.02, 1))   

combined_plot <- combined_plot +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        theme = theme(
            plot.title = element_text( 
                size = rel(2.2),
                family = fonts$title,
                face = "bold",
                color = colors$title,
                lineheight = 1.1,
                margin = margin(t = 5, b = 5)
            ),
            plot.subtitle = element_text(
                size = rel(1),  
                family = fonts$subtitle,
                color = colors$subtitle,
                lineheight = 1.2,
                margin = margin(t = 5, b = 10)
            ),
            plot.caption = element_markdown(
                size   = rel(0.75),
                family = fonts$caption,
                color  = colors$caption,
                hjust  = 0.5,
                margin = margin(t = 10)
            ),
            plot.margin = margin(t = 20, r = 10, b = 20, l = 10),
        ))

7. Save

Show code
### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2025, 
  week = 9, 
  width = 12,
  height = 10
)

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] ggridges_0.5.6  patchwork_1.3.0 camcorder_0.1.0 here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         htmlwidgets_1.6.4 
[17] bit_4.5.0          curl_6.0.0         xml2_1.3.6         repr_1.1.7        
[21] tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0         fansi_1.0.6       
[25] colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.3          rmarkdown_2.29    
[29] crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1  tzdb_0.4.0        
[33] commonmark_1.9.2   parallel_4.4.0     ggplotify_0.1.2    base64enc_0.1-3   
[37] vctrs_0.6.5        yulab.utils_0.1.8  jsonlite_1.8.9     gridGraphics_0.5-1
[41] hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0  magick_2.8.5      
[45] gifski_1.32.0-1    codetools_0.2-20   stringi_1.8.4      gtable_0.3.6      
[49] munsell_0.5.1      pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1 
[53] R6_2.5.1           httr2_1.0.6        rprojroot_2.0.4    vroom_1.6.5       
[57] evaluate_1.0.1     markdown_1.13      gridtext_0.1.5     snakecase_0.11.1  
[61] renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3      xfun_0.49         
[65] fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in tt_2025_09.qmd.

For the full repository, click here.

10. References

  1. Data Sources:

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